88 lines
3.7 KiB
R
88 lines
3.7 KiB
R
# Mixed ANOVA Analysis for Domain Means - EOHI2
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# EOHI Experiment Data Analysis - Domain Level Analysis with INTERVAL factor
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# Variables: NPast_5_pref_MEAN, NPast_5_pers_MEAN, NPast_5_val_MEAN, etc.
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# NFut_5_pref_MEAN, NFut_5_pers_MEAN, NFut_5_val_MEAN, etc.
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# NPast_10_pref_MEAN, NPast_10_pers_MEAN, NPast_10_val_MEAN, etc.
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# NFut_10_pref_MEAN, NFut_10_pers_MEAN, NFut_10_val_MEAN, etc.
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# 5.10past_pref_MEAN, 5.10past_pers_MEAN, 5.10past_val_MEAN
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# 5.10fut_pref_MEAN, 5.10fut_pers_MEAN, 5.10fut_val_MEAN
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# Load required libraries
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library(tidyverse)
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library(ez)
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library(car)
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library(afex) # For aov_ez (cleaner ANOVA output)
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library(nortest) # For normality tests
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library(emmeans) # For post-hoc comparisons
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library(purrr) # For map functions
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library(effsize) # For Cohen's d calculations
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library(effectsize) # For effect size calculations
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# Global options to remove scientific notation
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options(scipen = 999)
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# Set contrasts to sum for mixed ANOVA (necessary for proper interpretation)
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options(contrasts = c("contr.sum", "contr.poly"))
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi2")
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# Read the data
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data <- read.csv("eohi2.csv")
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# Display basic information about the dataset
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print(paste("Dataset dimensions:", paste(dim(data), collapse = " x")))
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print(paste("Number of participants:", length(unique(data$ResponseId))))
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# Verify the specific variables we need
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required_vars <- c("NPast_5_pref_MEAN", "NPast_5_pers_MEAN", "NPast_5_val_MEAN",
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"NPast_10_pref_MEAN", "NPast_10_pers_MEAN", "NPast_10_val_MEAN",
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"NFut_5_pref_MEAN", "NFut_5_pers_MEAN", "NFut_5_val_MEAN",
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"NFut_10_pref_MEAN", "NFut_10_pers_MEAN", "NFut_10_val_MEAN",
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"5.10past_pref_MEAN", "5.10past_pers_MEAN", "5.10past_val_MEAN",
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"5.10fut_pref_MEAN", "5.10fut_pers_MEAN", "5.10fut_val_MEAN")
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missing_vars <- required_vars[!required_vars %in% colnames(data)]
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if (length(missing_vars) > 0) {
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print(paste("Warning: Missing variables:", paste(missing_vars, collapse = ", ")))
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} else {
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print("All required domain mean variables found!")
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}
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# Define domain mapping with TIME, DOMAIN, and INTERVAL factors
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domain_mapping <- data.frame(
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variable = required_vars,
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time = c(rep("Past", 3), rep("Past", 3), rep("Future", 3), rep("Future", 3),
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rep("Past", 3), rep("Future", 3)),
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domain = rep(c("Preferences", "Personality", "Values"), 6),
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interval = c(rep("5", 3), rep("10", 3), rep("5", 3), rep("10", 3),
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rep("5_10", 3), rep("5_10", 3)),
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stringsAsFactors = FALSE
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)
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print("Domain mapping created:")
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print(domain_mapping)
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# Efficient data pivoting using pivot_longer
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long_data <- data %>%
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select(ResponseId, TEMPORAL_DO, INTERVAL_DO, all_of(required_vars)) %>%
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pivot_longer(
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cols = all_of(required_vars),
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names_to = "variable",
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values_to = "MEAN_DIFFERENCE"
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) %>%
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left_join(domain_mapping, by = "variable") %>%
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# Convert to factors with proper levels
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mutate(
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TIME = factor(time, levels = c("Past", "Future")),
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DOMAIN = factor(domain, levels = c("Preferences", "Personality", "Values")),
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INTERVAL = factor(interval, levels = c("5", "10", "5_10")),
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ResponseId = as.factor(ResponseId),
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TEMPORAL_DO = as.factor(TEMPORAL_DO),
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INTERVAL_DO = as.factor(INTERVAL_DO)
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) %>%
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# Select final columns and remove any rows with missing values
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select(ResponseId, TEMPORAL_DO, INTERVAL_DO, TIME, DOMAIN, INTERVAL, MEAN_DIFFERENCE) %>%
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filter(!is.na(MEAN_DIFFERENCE))
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print(paste("Long data dimensions:", paste(dim(long_data), collapse = " x")))
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print(paste("Number of participants:", length(unique(long_data$ResponseId)))) |